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首页> 外文期刊>Catalysis Letters >Statistical Product Selectivity Modeling and Optimization for γ-Al2O3-Supported Cobalt Catalysts-Based Fischer–Tropsch Synthesis
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Statistical Product Selectivity Modeling and Optimization for γ-Al2O3-Supported Cobalt Catalysts-Based Fischer–Tropsch Synthesis

机译：Statistical Product Selectivity Modeling and Optimization for γ-Al2O3-Supported Cobalt Catalysts-Based Fischer–Tropsch Synthesis

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Abstract The statistical selectivity models were developed for four different Fischer–Tropsch synthesis product range, including methane (CH4), light olefins (C2=C4), light paraffins (C2–C4), and long-chain hydrocarbons (C5+), based on the experimental data obtained over thirteen γ-Al2O3 supported cobalt-based catalysts with different cobalt particle and pore sizes. The input variables consist of cobalt metal particle size and catalyst pore size. The cubic and quadratic polynomial equations were fitted to the experimental data, however, the mathematical models were subjected to model reduction for the enhancement of model adequacy, which was investigated through ANOVA. The multi-objective optimization revealed that the maximum C5+?selectivity (84.150%) could be achieved at the cobalt particle size and pore sizes of 14.764 and 23.129?nm, respectively, while keeping the selectivity to other hydrocarbon products minimum.Graphic Abstract

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